4 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
Near-Optimal First-Order Regret for Bandits with Feedback Graphs
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph. For strongly observable graphs, [ACBDK15] establishes a minimax regret o…
Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
In this paper, we study Interaction-Grounded Learning (IGL) [Xie et al., 2021], a paradigm designed for realistic scenarios where the learner receives indirect feedback generated b…
Online Joint Fine-tuning of Multi-Agent Flows
Paul Mineiro
A Flow is a collection of component models ("Agents") which constructs the solution to a complex problem via iterative communication. Flows have emerged as state of the art archite…
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment an…